Everything You Need To Know About Big Data IBM stated that human race produces 2.5 quintillion bytes of data every day. This is the reason why “big data” became a common phrase. To know more about Big Data, keep reading.
What is big data? Big data is taking vast data from multiple sources, data of different kinds; often multiple types of data at the same time, and data that changed over time. Doing much quickly, in real time In the early days, the industry started an acronym to describe the three facets out of four: VVV, for volume (vast quantities), variety (different types of data), and velocity (speed).
Big data vs. the data warehouse But the acronym missed the point that the data don’t need to be permanently transformed for analysis. On a contrary, the data warehouse was created to analyze specific data for certain purposes, and the data was converted to specific formats, with the original data essentially destroyed in the process. But don’t consider a data makes the data warehouse obsolete. Big data systems work with unstructured data as it comes, but the kind of query results you get not similar to the sophistication of data warehouse. Big data allows analyzing more data from various sources but at less resolution. The technology breakthroughs behind big data To accomplish the four facets of big data, required several technology breakthroughs, including creation of a distributed file system called Hadoop, a method to make sense of disparate data on the fly (first Google’s MapReduce, and more recently Apache Spark), and a cloud/internet infrastructure for accessing and moving the data as needed. Until some years ago, it wasn’t possible to manipulate more than a relatively small amount of data at one time. Limitations were on the quantity and location of data storage, ability to handle different data formats from multiple sources and computing power that made the tasks impossible. Later in 2003, researchers at Google developed MapReduce. This technique simplified how to deal with large data sets by first mapping the data to a series of key/value pairs, then doing calculations on similar keys to lower them to a single value. This allowed Google to produce faster search results from increasingly larger volumes of data. At about 2003, Google created the two breakthroughs that made big data possible: One is Hadoop, which has two key services: Reliable data storage using Hadoop Distributed File System (HDFS) High-performance parallel data processing using Map Reduce.
Hadoop functions on a collection of the commodity. There is a chance to add or delete servers in a Hadoop cluster; the system will detect and compensate for hardware or system problems on any server. Hadoop can deliver data and run large-scale, high-performance processing jobs in spite of system changes or failures. Hadoop offers sub projects, which adds functions and new capabilities to the platform: Hadoop Common: They support other Hadoop sub projects. Chukwa: A system of data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports organized data storage for large tables. HDFS: A distributed le system that provides high throughput access to application data. Hive: This offers data summarization and impromptu queries. MapReduce: A software for distributed processing of large data sets. Pig: A high-level data language and framework for execution for parallel computation. ZooKeeper: A high-performance service for coordination for distributed applications. MapReduce helps you to write codes that process the vast quantity of unstructured data across a distributed cluster of processors or stand-alone computers. The MapReduce framework has two functional areas: Map, a function that sends work to different nodes in the distributed cluster. Lower, a function that collects the work and brings the results into a single value. One of MapReduce’s main advantages is that it is tolerant to faults. Apache Hadoop, an open-source framework which uses MapReduce, was designed two years later. Originally created to index the Nutch search engine, Hadoop is presently used in virtually every major industry. In 2009, University of California, Berkeley researchers created Apache Spark as an alternative to MapReduce. As Spark does calculations in parallel using storage memory, it could be 100 times faster than MapReduce. With Hadoop, yet you need to store and access data. And this is done through NoSQL database like MongoDB, like CouchDB, or Cassandra, which can handle unstructured or semi-structured data distributed across multiple machines. Tools like Tableau, Splunk, and Jasper BI helps you resolve that data to identify patterns, extract meaning, and find out new insights. Now we conclude, keep checking our space. If you liked this article, then keep visiting our website and remain connected by liking our Facebook page ForumMantra.